##// END OF EJS Templates
remove: properly set return code when warnings are issued...
remove: properly set return code when warnings are issued This removes the warn() function in favor of issuing warnings directly for each kind of file that Mercurial won't remove. This also uses three separate translatable strings instead of using string formatting to build the message. This should make it easier to localize.

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bdiff.py
78 lines | 2.0 KiB | text/x-python | PythonLexer
# bdiff.py - Python implementation of bdiff.c
#
# Copyright 2009 Matt Mackall <mpm@selenic.com> and others
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
import struct, difflib
def splitnewlines(text):
'''like str.splitlines, but only split on newlines.'''
lines = [l + '\n' for l in text.split('\n')]
if lines:
if lines[-1] == '\n':
lines.pop()
else:
lines[-1] = lines[-1][:-1]
return lines
def _normalizeblocks(a, b, blocks):
prev = None
for curr in blocks:
if prev is None:
prev = curr
continue
shift = 0
a1, b1, l1 = prev
a1end = a1 + l1
b1end = b1 + l1
a2, b2, l2 = curr
a2end = a2 + l2
b2end = b2 + l2
if a1end == a2:
while (a1end + shift < a2end and
a[a1end + shift] == b[b1end + shift]):
shift += 1
elif b1end == b2:
while (b1end + shift < b2end and
a[a1end + shift] == b[b1end + shift]):
shift += 1
yield a1, b1, l1 + shift
prev = a2 + shift, b2 + shift, l2 - shift
yield prev
def bdiff(a, b):
a = str(a).splitlines(True)
b = str(b).splitlines(True)
if not a:
s = "".join(b)
return s and (struct.pack(">lll", 0, 0, len(s)) + s)
bin = []
p = [0]
for i in a: p.append(p[-1] + len(i))
d = difflib.SequenceMatcher(None, a, b).get_matching_blocks()
d = _normalizeblocks(a, b, d)
la = 0
lb = 0
for am, bm, size in d:
s = "".join(b[lb:bm])
if am > la or s:
bin.append(struct.pack(">lll", p[la], p[am], len(s)) + s)
la = am + size
lb = bm + size
return "".join(bin)
def blocks(a, b):
an = splitnewlines(a)
bn = splitnewlines(b)
d = difflib.SequenceMatcher(None, an, bn).get_matching_blocks()
d = _normalizeblocks(an, bn, d)
return [(i, i + n, j, j + n) for (i, j, n) in d]